Call For Papers

Call for Papers

The Modern Machine Learning & Natural Language Processing Workshop will be held in conjunction with Neural Information Processing Systems (NIPS) on December 12, 2014 in Palais des Congrès de Montréal, Montreal, Quebec, Canada.

Overview:

The structure, complexity, and sheer diversity and variety of human language makes Natural Language Processing (NLP) distinct from other areas of AI. Certain core NLP problems (e.g., sequence tagging and syntactic parsing) have a long history of being abstracted into machine learning (ML) formulations and have thus traditionally been an inspiration for ML-driven solutions, leading to a transfer of ideas from one field to the other. Problems in NLP are also appealing to those working on core ML research problems due to the high-dimensional and sparse nature of these problems and the need for robust solutions that gracefully handle noise in the training data.

But there are many other areas within NLP where the ML community is less involved, such as semantics, discourse and pragmatics analysis, summarization, and parts of machine translation, and that continue to rely on linguistically-motivated but imprecise heuristics which may benefit from new ML approaches. Similarly, there are new developments and paradigms in ML that may be particularly appropriate for NLP, but which have not been explored in this domain. Our aim in this workshop is to bridge this gap. and showcase the latest advances at the intersection of ML and NLP and address core questions that are relevant to researchers in both communities.

The workshop invites paper submissions that will be presented in poster format. Topics of interest include (but are not limited to):
- representation learning for NLP
- novel theoretical ideas with assumptions suitable to NLP
- scalable inference/optimization techniques
- weakly-supervised approaches to handle lack of annotated data in complex structured prediction tasks
- problems in multilinguality, NLP for social media, discourse analysis, semantics, and other areas that would benefit from ML approaches and analysis

Submissions should be written as extended abstracts, no longer than 4 pages (excluding references) in the NIPS latex style. Relevant work previously presented in other conferences is encouraged, though submitters should note this in their submission. All submissions should be emailed to nips2014mlnlp@gmail.com